Time-resolved pre-treatment portal dosimetry systems, devices, and methods

ABSTRACT

Systems, devices, and methods for pre-treatment verification of radiation dose delivery in arc-based radiation therapy devices using a time-dependent gamma evaluation method.

FIELD

The present disclosure relates generally to delivering radiation to apatient, and more particularly to systems, methods, and computer programproducts for providing time dependent pre-treatment dosimetricverification of dynamic radiation therapy treatments using an electronicportal imaging device (EPID). The present disclosure also relates tosystems, methods, and computer program products for providingtime-dependent gamma analysis on dynamic data.

BACKGROUND

Dynamic radiation treatment techniques, such as intensity-modulatedradiation therapy (IMRT) and volumetric modulated arc therapy (VMAT),are typically used with a radiotherapy system, such as a linearaccelerator (linac), equipped with a multileaf collimator (MLC) to treatpathological anatomies (tumors, lesions, vascular malformations, nervedisorders, etc.) by delivering prescribed doses of radiation (X-rays,gamma rays, electrons, protons, and/or ions) to the pathological anatomywhile minimizing radiation exposure to the surrounding tissue andcritical anatomical structures. Use of the multileaf collimator allowsthe radio therapist to treat a patient from multiple angles whilevarying the shape and dose of the radiation beam, thereby providing agreatly enhanced ability to deliver radiation to a target within atreatment volume while avoiding excess irradiation of nearby healthytissue. Intensity-modulated radiation therapy (IMRT) and volumetricmodulated arc therapy (VMAT), which are complex techniques involving thesynchronous occurrence of gantry rotation, multileaf collimator motion,and dose rate modulation, are rapidly growing as radiation therapytechniques due to their ability to quickly deliver highly conformal dosedistributions.

Because of the high complexity and uniqueness of (IMRT) and (VMAT)treatment plans, patient-specific pre-treatment (i.e., without thepatient in the beam) verification is generally considered a necessaryprerequisite to patient treatment. Pre-treatment verification includesprocedures to compare the whole or at least part of the intendedtreatment plan with measurements of corresponding radiation beamsdelivered by the linear accelerator (linac) outside the patienttreatment time.

Dosimetric verification is one of the pre-treatment protocolsimplemented for (IMRT) and (VMAT) treatments. Dosimetric verificationincludes verification that the dose distribution delivered is in factthe dose distribution predicted to be delivered to the patient. Becauseof the increased beam delivery complexity offered by (IMRT) and (VMAT)treatments, dosimetric verification for (IMRT) and (VMAT) treatmentsrequire rigorous verification of the absolute dose delivery. Currentlyavailable dosimetric verification methods, such as film dosimetry inphantoms and ion chamber point dose verification, however, result ineither integrated dose or relative dose distribution verification, andnot absolute dose verification. Also, these methods are time-consuming,cumbersome, and error-prone.

In established EPID-based pre-treatment dose verification methods,integrated images are compared against dose images predicted by thetreatment planning system (TPS). Complex dynamic therapy techniques,however, require a more detailed verification based on cine imageseries. Therefore, in such complex treatments it is not enough tocompare the integrated images against predicted dose images. Instead,the acquired dose images, converted to absolute dose distributions, needto be compared with the predicted dose images in a time-resolved manner.

Currently available comparison methods, such as the two component gammafunction, are not suitable for time-dependent (i.e., dynamic) datacomparison. Therefore, the existing gamma function is also not suitablefor comparing the acquired dose images with the predicted dose images ina time-resolved manner.

SUMMARY

The present disclosure provides systems, methods, and computer programproducts for electronic portal imaging device (EPID)-based pre-treatmentdose verification for dynamic treatment plans that allow for comparisonof absolute dose distributions with predicted dose distributions in atime-resolved manner.

The present disclosure also provides systems, methods, and computerprogram products for (EPID)-based pre-treatment dose verification forcomplex dynamic treatment plans, such as, but not limited to (IMRT) and(VMAT) treatment plans.

The present disclosure also provides systems, methods, devices, andcomputer program products for time dependent pre-treatment doseverification using electronic portal imaging devices (EPIDs) for dynamictreatments using both flattened and flattening filter free (FFF) beams.

The present disclosure also provides an (EPID) calibration model forconverting measured dose distributions into absolute dose distributions.

The present disclosure also provides systems, methods, and computerprogram products for quantitative evaluation of dose distributions.

The present disclosure also provides systems, methods, and computerprogram products for evaluating dose distributions using a 4Dtime-dependent gamma function.

The present disclosure also provides a non-transitory computer-readablestorage medium upon which is embodied a sequence of programmedinstructions for quality control in a radiation therapy treatment systemas disclosed herein, including a computer processing system, asdisclosed herein, which executes the sequence of programmed instructionsembodied on the computer-readable storage medium to cause the computerprocessing system to perform the steps of the methods as disclosedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.The invention will be best understood by reading the ensuingspecification in conjunction with the drawing figures, in which likeelements are designated by like reference numerals. As used herein,various embodiments can mean some or all embodiments.

FIG. 1 is a perspective view of a dynamic radiation therapy systemaccording to various embodiments of the invention.

FIG. 2 illustrates predicted portal dose images generated per controlpoint (CP) according to various embodiments.

FIG. 3 is a pixel sensitivity correction profile graph.

FIG. 4 is an off-axis ratio correction profile graph.

FIG. 5 is a diagram of energy deposition kernels.

FIG. 6 is a graph of measured and simulated point dose values.

FIG. 7 is a graph of measured and simulated dose profiles for on andoff-axis radiation fields.

FIG. 8 illustrates a method for checking deviations of resampled imagesaccording to various embodiments.

FIG. 9 illustrates a method for direct comparison between predicted andmeasured radiation doses per control points (CPs) according to variousembodiments.

FIG. 10 illustrates a gamma distribution between measured and predicteddoses per control points (CPs) according to various embodiments.

FIG. 11 illustrates a percentage of in-field area failing the gammacriterion according to various embodiments.

FIG. 12 is a flow chart of a dosimetric evaluation procedure accordingto various embodiments.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary radiation therapy treatment system 100that can provide radiation therapy to a patient 5 positioned on atreatment couch 1, and allow for the implementation of variouspre-treatment portal dosimetry verifications for quality assurance (QA)protocols. The radiation therapy treatment can include photon-basedradiation therapy, particle therapy, electron beam therapy, or any othertype of treatment therapy. In an embodiment, the radiation therapytreatment system 100 includes a radiation treatment device 10, such as,but not limited to, a radiotherapy or radiosurgery device, which caninclude a gantry 7 supporting a radiation module 8 which includes one ormore radiation sources 3 and a linear accelerator (linac) 2 operable togenerate a beam of kV or MV X-ray radiation. The gantry 7 can be a ringgantry (i.e., it extends through a full 360 degree arc to create acomplete ring or circle), but other types of mounting arrangements mayalso be employed. For example, a static beam, or a C-type, partial ringgantry, or robotic arm could be used. Any other framework capable ofpositioning the radiation module 8 at various rotational and/or axialpositions relative to the patient 5 may also be used.

The radiation module 8 can also include a modulation device (not shown)operable to modulate the radiation beam as well as to direct atherapeutic radiation beam toward the patient 5 and toward a portion ofthe patient which is desired to be irradiated. The portion desired to beirradiated is referred to as the target or target region or a region ofinterest. The patient 5 may have one or more regions of interest thatneed to be irradiated. A collimation device (not shown) may be includedin the modulation device to define and adjust the size of an aperturethrough which the radiation beam may pass from the source 3 toward thepatient 5. The collimation device may be controlled by an actuator (notshown) which can be controlled by a computer processing system 40 and/ora controller 30.

In an embodiment, the radiation therapy device is a kV or MV energyintensity modulated radiotherapy (IMRT) device. The intensity profilesin such a system are tailored to the treatment requirements of theindividual patient. The intensity modulated radiotherapy fields aredelivered with a multi-leaf collimator (MLC), which can be acomputer-controlled mechanical beam shaping device attached to the headof the linear accelerator and includes an assembly of metal fingers orleaves. The (MLC) can be made of 120 movable leaves with 0.5 and/or 1.0cm leaf width, for example. For each beam direction, the optimizedintensity profile is realized by sequential delivery of varioussubfields with optimized shapes and weights. From one subfield to thenext, the leaves may move with the radiation beam on (i.e., dynamicmultileaf collimation (DMLC)) or with the radiation beam off (i.e.,segmented multileaf collimation (SMLC). The device 10 can also be atomotherapy device where intensity modulation is achieved with a binarycollimator which opens and closes under computer control. As the gantrycontinuously rotates around the patient, the exposure time of a smallwidth of the beam can be adjusted with the opening and closing of thebinary collimator, allowing the radiation to be delivered to the tumorthrough the most preferred directions and locations of the patient.

The device 10 can also be a helical tomotherapy device which includes aslip-ring rotating gantry. The device 10 can also be an intensitymodulated arc therapy device (IMAT) where instead of using rotating fanbeams, rotational cone beams of varying shapes are used to achieveintensity modulation. The device 10 can also be a simplified intensitymodulated arc therapy (SIMAT) device which uses multiple arcs, or asweeping window arc therapy device (SWAT), where the (MLC) leafpositions sweep across the target planning volume (TPV) with rotation.The device 10 can also be a volumetric modulated arc therapy (VMAT)device where dose rate, beam aperture shape, and the speed of rotationcan be continuously varied to deliver the prescribed dose to the targetplanning volume (TPV).

Although illustrative embodiments include (VMAT) as the treatment device10, any type of intensity modulated radiotherapy (IMRT) device can beused. Each type of device 10 is accompanied by a corresponding radiationplan and radiation delivery procedure.

The device 10 further includes a portal dose imaging device 20 foracquiring digital images to be used for portal dosimetry verification.The portal dose imaging device 20 can be an electronic portal imagingdevice (EPID). The portal dose imaging device 20 can be placed atdifferent locations, such as, on top of the treatment couch 1, orattached to the accelerator head 2, for example. The portal dose imagingdevice 20 can generate immediate 2D digital information. It can be acamera-based device, such as a camera-based (EPID), or an amorphoussilicon based device, such as an amorphous silicon (EPID). The (EPID) 20can also be a CCD-camera based (EPID), which is effectively an array ofsimultaneously integrating dosimeters with a dead time in betweenacquired frames of about 0.1 ms, for example. Another alternative is aflat panel imager (or amorphous silicon EPID), which offers good imagequality, high optical transfer efficiency, large imaging area, andresistance to radiation.

An exemplary amorphous silicon imaging device that can be used is aaSi1000 EPID imager that has arrays of light sensitive amorphous-Siphotodiodes arranged in 40×30 cm² active detector area 4 and has amaximum frame rate of 9.574 fps, each frame being a scan of the detectorelements. The flat panel imager generally consists of picture elements(pixels) that register the amount of radiation that falls on them andconvert the received amount of radiation into a corresponding number ofelectrons. The electrons are converted into electrical signals which arefurther processed using either the imaging device 20 or a computer 40.Such a configuration (i.e., digital imaging detector(s) positionedopposite the therapeutic source(s)) provides the ability to continuouslyand immediately capture the energy and intensity of the therapeuticradiation transmitted from each arc field segment and/or during acontinuous arc beam delivery, in order to generate two-dimensional (2D)images of digitized X-ray measurements. Because the portal dose imagingdevice 20 generates immediate, 2D digital information, it facilitates 2Ddosimetry at any gantry angle.

The computer 40 includes typical hardware such as a processor, and anoperating system for running various software programs and/orcommunication applications. The computer can include software programsthat operate to communicate with the radiation therapy device 10, andthe software programs are also operable to receive data from anyexternal software programs and hardware. The computer 40 can alsoinclude any suitable input/output devices adapted to be accessed bymedical personnel, as well as I/O interfaces, storage devices, memory,keyboard, mouse, monitor, printers, scanner, etc. The computer 40 canalso be networked with other computers and radiation therapy systems.Both the radiation therapy device 10 and the computer 40 can communicatewith a network as well as a database and servers. The computer 40 isalso adapted to transfer medical image related data between differentpieces of medical equipment.

The system 100 can also include a plurality of modules containingprogrammed instructions which communicate with each other and cause thesystem 100 to perform different functions related to radiationtherapy/surgery, as discussed herein, when executed. For example, thesystem 100 can include a treatment plan module operable to generate thetreatment plan for the patient 5 based on a plurality of data input tothe system by the medical personnel, the treatment plan including apredicted radiation dose distribution, a patient positioning moduleoperable to position and align the patient 5 with respect to theisocenter of the gantry 7 for a particular radiation therapy treatment,an image acquiring module operable to instruct the radiation therapydevice 10 to acquire images of the patient 5 prior to the radiationtherapy treatment and/or during the radiation therapy treatment (i.e.,in-vivo images), and/or to instruct other imaging devices or systems toacquire images of the patient 5.

The system 100 can further include a treatment delivery module operableto instruct the radiation therapy device 10 to deliver the treatmentplan with or without the patient 5 in place, a converting moduleoperable to convert the 2D portal images (EPIs) into 2D portal doses, ananalysis module operable to compute comparisons between predicted andmeasured dose distributions, and a calculation module operable tocalculate dose delivery errors. The analysis module can further includecomputational algorithms to quantitatively compare the measured and thepredicted dose distributions in a time-resolved manner. The modules canbe written in the C or C++ programming languages, for example. Computerprogram code for carrying out operations of the invention as describedherein may also be written in other programming languages.

As part of the quality control protocol, for pre-treatment portaldosimetry verification, the radiation dose distribution delivered by thetreatment fields is validated before starting the patient treatment.Patient treatment involves irradiating the patient with treatment beams(i.e., X-rays, for example) according to a prescribed delivery treatmentplan. The prescribed delivery plan is developed using a treatmentplanning system (TPS) prior to the treatment phase, and involvesdeveloping a plan using special computer software to optimally irradiatethe tumor and minimize dose to the surrounding normal tissue fromdifferent angles and planes. The treatment plan includes a trajectory(motion path) for the radiation beam computed to deliver a dosedistribution that the treating physician finds acceptable, taking intoaccount a variety of medical constraints. The beam trajectory isdeveloped based on knowledge of the exact coordinates of the targetwithin the anatomical structure, and the exact shape and size of thetumor or abnormality within the body. For arc therapy (e.g., dynamictreatment), a trajectory may be an arc, typically a single 360 degree,or a single 180 degree revolution, formed by the rotation of thetreatment gantry about the patient.

To optimize arc delivery treatment plans, at the outset of the treatmentplanning process, a number of control points (CPs) along the trajectoryare specified. Each control point (CP) is associated with a set oftreatment parameters, including but not limited to, a set of (MLC) leafpositions, (MLC) shape, gantry rotation speed, gantry position, doserate, and/or any other parameters. The number and position of thecontrol points (CPs) may be set in any convenient manner, such as, butnot limited to, by using the treatment planning software, or by thesystem operator. In an exemplary embodiment, the trajectory can includea single 180 degree arc trajectory and approximately 177 sequentialcontrol points (CPs), which means that there are 177 configurations thatthe linac 2 should conform to in order to deliver the planned treatment.

Based on the treatment parameters, a dose distribution within thetreatment volume is calculated for each control point (CP) by any numberof techniques, such as, but not limited to, pencil beam convolution, orany other suitable algorithm. In order to generate a predicted portaldose image for an arc segment located between two consecutive controlpoints (“predicted image per CP”), once the treatment plan is completed,the radiation dose distribution for each (CP) is associated with thecorresponding gantry angle, (MLC) configuration, and monitor unit (MU)extracted from the system's RTPLAN file. The RTPLAN is a treatmentplanning module that can include a plurality of radiotherapy (RT)modules associated with the processor 40 that work together to addressthe requirements for transfer of treatment plans before or during acourse of treatment. The modules can include information about thegeneral treatment plan, prescription, tolerance tables patient setup,fraction scheme, beams, etc. By extracting the gantry angle, (MLC)configuration, and the monitor unit (MU) for each control point (CP)from the RTPLAN file and associating the extracted (CP) parameters withthe corresponding calculated dose distributions for each (CP), each (CP)is in effect treated as a static field. By treating each (CP) as astatic field, a predicted portal dose image for each field (i.e., foreach CP) can be generated, as shown in FIG. 2. By generating a predicteddose image for each (CP), a sequence of 2D predicted portal dose imagesare obtained, each predicted portal dose image in the sequencecorresponding to a respective (CP). The generated sequence of predictedportal dose images can be stored in the computer processor 40.

After the treatment planning, and before treatment delivery, thepre-treatment dose validation described herein is executed as part ofthe quality assurance protocol. The pre-treatment dosimetric validationprocess includes delivering the radiation beam, absent the patient, ontothe EPID 20 as per the treatment plan, continuously measuring thedelivered radiation dose, and comparing the measured dose with thepredicted dose. A method to accomplish this pre-treatment dosimetricevaluation in a time-resolved fashion and in near real-time, the methodincludes the following steps: (1) during a continuous arc beam delivery,which could be a 180 degree arc for example, 2D portal images (EPIs)G′(x, y) are continuously acquired using the electronic portal imagingdevice (EPID) 20; (2) the acquired 2D portal images are converted into2D absolute portal dose images (PDIs) D_(p)(x, y) using a dosimetriccalibration model described herein; (3) the image frames are resampledinto respective time divisions corresponding to the treatment plan'scontrol points (CPs) to obtain a sequence of 2D measured portal doseimages, each measured portal dose image in the sequence corresponding toa respective (CP); and (4) each of the measured portal dose images arecompared with corresponding predicted portal dose images using atime-dependent gamma evaluation function. Each of the enumerated stepsare described in detail herein.

To generate the sequence of portal images, the EPID 20 receives datafrom different projection angles θ≦θ≦360° as the gantry 7 rotates,collects the transmitted radiation, and continuously generatestwo-dimensional (2D) digital portal images (EPI) G′(x, y). Each portalimage (EPI) G′(x, y) is generated under the same condition as is plannedfor the actual treatment, but without the patient placed in the beam.The EPID images G′(x, y) are captured in a continuous dosimetric fashionwithout syncing the beam pulses and the EPID readout in order to providea plurality of raw 2D portal images (i.e., a fluoroscopic imagesequence). The system 100 can further include a frame grabber card (notshown) and associated hardware and software tools (not shown) whichallow the raw image frames to be directly exported from the EPID to thecomputer 40 before any correction is applied. The system 100 furtherincludes a synching module configured to associate the acquired imageframes with the treatment information (i.e., plan identification, planparameters, etc.), and to resample the image frames into correct timedivisions corresponding to the treatment plans' control points (CPs).

The continuously acquired EPID images G′(x, y) are converted into 2Dabsolute dose sequences (i.e., dose film) using a dosimetric calibrationmodel, as shown in eq. 2. The absolute portal dose images D_(p)(x, y) soobtained represent absolute dose distributions at the plane of the EPID,and are obtained by converting gray scale pixel values to dose values orsimulation of the gray scale pixel values. By continuously convertingthe measured EPID images G′(x, y) into absolute portal dose images(PDIs) D_(p)(x, y), a sequence of measured absolute portal dose imagesis obtained. However, prior to converting the measured EPID images intoabsolute portal dose images, the measured EPID images G′(x, y) can becorrected for the non-uniform arm back-scatter present in certainsupport arm designs of the radiation device 10. The correction is doneusing an implementation of the convergent Van Cittert approximatedeconvolution, shown in eq. 1.G′(x,y)_(n+1) =G′(x,y)_(n)−μ(G _(DD)(x,y)−(G′(x,y)_(n) ·AM(x,y)

K _(BS)(i,j)))  (1)Where n is an iteration number, G′_(n+1) represents the convergentsolution and μ is a relaxation factor. G_(DD) represents the initialEPID image which is corrected for dead pixels and dark field offset(i.e., the signal from the EPID when no radiation is incident on it).G_(DD) is used as the initial estimate (i.e., G′_(n=0)=G_(DD)). AM isthe mask of the EPID arm, K_(BS) is the backscatter kernel, and

denotes a convolution. The coordinates (x, y) are relative to the beamaxis where (i, j) are relative to the kernel center.

The absolute portal dose images D_(p)(x, y) are then generated using theback-scatter corrected portal images and the following portal dosereconstruction algorithm (i.e., EPID calibration model):D _(p)(x,y)=(C _(F) ⁻¹ ·G(t _(rad))⁻¹ ·G′(x,y)·PS⁻¹(h,v)·OAR⁻¹(x,y)

⁻¹ K _(F)(i,j))

⁻¹ K _(P)(i,j)  (2)Thus, each absolute portal dose image D_(p)(x, y) is obtained bycorrecting each corresponding measured (and arm-scatter corrected) EPIDimage G′(x, y) for ghosting (G(t_(rad))), pixel sensitivity (PS), andoff-axis ratio (OAR), followed by de-convolving with in-field andpenumbra energy deposition kernels (K_(F) and K_(P) respectively). C_(F)is the absolute conversion factor of EPID pixels to dose under areference condition. The coordinates (h, v) are the pixel index on theEPID. Each of the correction factors are described below.1. A Dead Pixel (DP) Correction.

Over time/use a small number of individual EPID pixels are permanentlydamaged and stop responding to radiation. To correct for these deadpixels, each one can be identified using a threshold bitmask, and anaverage value, derived from the surrounding pixels, can be used toreplace the erroneous value. All EPID frames can be corrected for deadpixels.

2. The Dark Field Correction (G_(DD)=G_(raw)−DF).

This is to remove any persistent signal from the EPID images which ispresent when no radiation is incident on it. This is the dark field (DF)image, also known as the offset image. The noiseless DF image can beobtained by averaging 100 frames without a beam. This correction can beapplied automatically when using clinical acquisition software. All rawgreyscale EPID images (G_(raw)) can be corrected by subtracting the DFimage.

3. Support Arm Backscatter (ABS).

This correction is to obtain quantitative images as the design of theEPID support arm may result in non-uniform backscatter. The correctionis applied via eq. 1 shown above, but to derive the parameters of theback scatter kernel (K_(BS)) and the vertices of the arm mask (AM), aniterative process can be applied. As an exemplary process, a number offields are measured and an initial estimation of the shape of thesupport arm can be provided as a binary mask along with an initial guessof the single Gaussian backscatter kernel. A backscatter corrected imagecan be generated of any field using the resultant mask and backscatterkernel and the convergent method shown in eq. 1.

4. Pixel Sensitivity (PS).

This refers to the fact that the response to a uniform photon fluencevaries from pixel to pixel. The relative difference can be corrected byusing a pixel sensitivity map. A pixel sensitivity map can be generatedby first exposing different areas of the EPID to the same portion of abeam, which has a constant beam profile and spectrum. Flood images (FFimage) can also be acquired. A composite cross-line 1D profile can beformed from the central cross-line 2 cm, for example, of each field toobtain the sensitivity of the central row of pixels. The ratio profile(RP) of the beam can be obtained by dividing the corresponding row ofpixels of the FF image with the composite 1D pixel sensitivity profile.A profile of the FF image is shown as the dotted line in FIG. 3. Noisesand discontinuities, if present, can be reduced by fitting a 2nd orderpolynomial function to the data and extending beyond the measured regionshown as the dashed line in FIG. 3. As the beam is assumed to beradially symmetric, a 2D ratio image can be obtained by sampling the 1D(RP) in a radial fashion. The 2D pixel sensitivity map can then beobtained by dividing the FF image by the ratio image and scaling thevalues to be relative to the central on-axis value. A profile of theobtained sensitivity map can be shown as the solid line in FIG. 3. TheFF image contains (OAR), the beam profile (BP), and (PS). The ratioimage contains only the (OAR) and (BP). By dividing the two images, the(PS) can be obtained. All further calibration images can be correctedfor pixel sensitivity before additional calculation.

5. The Off-Axis Ratio (OAR).

This is the relative EPID response due to the change in beam spectrumoff-axis. The off-axis variation in pre-treatment beams is primarily dueto the flattening filter and target, but also includes inherent beamfeatures and scatter. This variation can be corrected to obtain reliabledistributions further away from the central beam axis. Once correctedfor DP, DF, ABS, and PS, the EPID images still contain the BP and theOAR. While keeping the EPID static, a number of rectangular fields canbe imaged, each shifted 2 cm from the last in the cross-line direction.A composite cross-line profile can be formed using the centralcross-line 2 cm of pixels for each field. An ionization chamber, forexample, can be used to obtain the corresponding central field dose foreach of the fields and a surrogate beam profile can be created fromthese values. The 1D OAR profile can then be obtained by dividing thecomposite cross-line profile by the ionization chamber obtainedsurrogate beam profile. Again a 2nd order polynomial function can befitted to the 1D OAR (shown in dashed in FIG. 4), extended beyond themeasured region (to encompass all possible positions of the EPID) andsampled in a radial fashion to obtain a 2D OAR distribution. All furthercalibration images can be corrected for OAR before additionalcalculation.

6. The Absolute Conversion Factor (CF).

This is the absolute conversion between EPID response and absolute ionchamber measurements. All other correction factors are relative to thismeasurement.

7. Field Size Energy Deposition Kernels (K_(F) and K_(p)).

Due to the scatter from the EPID itself, the pattern of energydeposition should be known so that the incident point dose image can beobtained. As the energy deposition pattern varies for changing fieldsizes, due to changes in scatter from both the linac head and the EPIDitself, a deposition pattern that can account for the change in fieldsize is required. This can be achieved using energy deposition kernelsfor the in-field and penumbra regions, and a single out of field factor.To derive the parameters for the energy deposition kernels, EPID andionization chamber measurements can be performed for each energy andsource-detector distance (SDD). A plurality of on-axis fields can beimaged with the EPID. The fields can be measured at their field centreseparately using an ionization chamber to obtain a central 1D in-lineprofile of each field. As illustrated in FIG. 5, the in-field kernel caninclude 2 radially extended exponential functions, one narrow peaked,and one broader, shallower function. Field masks can be created for thein-field, and out-of-field regions.

A portal dose image (PDI) is created for each of the fields imaged usingthe EPID. This can be done by applying all corrections (DP, DF, ABS, PS,OAR, and CF) to the measured EPID images. Then using initial, arbitrary,values for the kernel parameters, each field is deconvolved with thekernels resulting in a portal dose image (PDI). A central field dose canbe sampled and compared to the ionization chamber measured values forthose fields, shown in FIG. 6. From each field (on and off axis) PDI, a1D in-line profile can be sampled and compared to the scanningionization chamber measured values, shown in FIG. 7. By allowing theparameters of both kernels to vary (K_(f) and K_(P) in eq. 2), aniterative process can be used to obtain the best fitting values for eachby minimizing the deviations with a weighting process. Once kernelparameters are found, they can be used to deconvolve any open field atthat energy and SDD.

8. The Ghosting Correction (G(t_(rad))).

This compensates for any non-linearity in EPID response to varying MU.There are different mechanisms that contribute to the non-linearity.Charge trapping in the photodiode surface or bulk modulus can increaseimage lag (residual images from previous frames are visible in thecurrent frame) or can change the electric field within the photodiode,increasing the sensitivity of the detector (the panel's responseincreases as it is irradiated). The influence of these effects differsfor the different constructions of EPID.

The ghosting correction for the method presented here has little or noinfluence on the resulting images. As a result, the correction is notapplied for (VMAT) or dynamic (IMRT) fields but is still applied tostatic field treatments.

Control Point Synchronisation

Using the EPID calibration model as described above, absolute doseportal images (i.e., measured portal dose images) are obtained for theEPID images continuously acquired during the single 360 degree arcgantry rotation and radiation field delivery. In order to compare themeasured portal dose images with the predicted portal dose images, whichare the predicted dose images at control points (CPs), the 2D absoluteportal dose images D_(p)(x, y) are grouped into corresponding controlpoint (CP) divisions. The (CP) grouping is facilitated by two sets ofdata. One set of data is contained in the linac's trajectory log files,and another set of data is contained in the EPID frame header. The linactrajectory log files contain, among other parameters, a sampling of the(CP), (MLC) position, gantry angle, and (MU) every 20 ms, for example.The EPID frame header data contains a sampling of (MU) and gantry angleevery ˜104 ms, for example. The trajectory log files can be used todivide the treatment plan delivery time into (CP) divisions. Further,the beam-on time can be synchronised between the log files and the EPIDframes using the sampled (MU) values. The EPID frames can then beresampled using the timing data from the EPID frame file headers intocontrol point (CP) sized portal dose images (PDIs). By resampling theEPID frames, the measured absolute portal dose images, are grouped intocorresponding (CPs). The so obtained series of measured absolute portaldose images, each of the measured absolute portal dose images in theseries corresponding to a respective (CP), can be stored in the computer40. The reliability of the resampling can be checked using thedifferential (MU) values in both the trajectory log file and the EPIDframe header. Both sets of values come from the linac's monitor chamberand so any large deviations can be easily detected, as shown in FIG. 8.

The Time Dependent Gamma Comparison

Since both, the measured and the predicted portal dose images, aregrouped into corresponding (CPs), for each (CP), a direct comparisonbetween the measured and the predicted dose can be made, as shown inFIG. 9. Using a comparison algorithm, the differences between each ofthe predicted and corresponding measured portal dose images can bedetermined, and the discrepancies between the corresponding predictedand measured portal dose images be displayed and further evaluated, asillustrated in FIG. 9. The time-dependent comparison described herein,therefore, allows for the detection of discrepancies between themeasured and predicted dose distributions that otherwise would have beenaveraged out (i.e., if the images were integrated instead of groupingthem in time divisions). As shown in FIG. 9, integrated predicted portaldose images as well as integrated measured portal dose images can alsobe generated and compared with each other. However, as shown in FIG. 9,the time-dependent comparison highlights discrepancies that are notshown when only the integrated images are compared.

Instead of a direct comparison between the measured and correspondingpredicted portal dose images, a time-dependent gamma function describedherein can be used to evaluate the differences between the predicted andmeasured portal dose images.

Gamma evaluation is a method generally used to quantitatively comparedose distributions. The conventional gamma method uses a comparisonbetween a measured and predicted dose distribution. Generally, the gammaevaluation method combines a dose difference criterion with adistance-to-agreement (DTA) criterion which makes it a suitable methodfor both low and high dose gradient regions. Dose distributions can besubdivided into regions of low and high dose gradients, each with adifferent acceptance criterion. High dose gradients could be regionsdefined as pixels with maximum relative dose differences above 10% forneighboring pixels, for example. In high dose gradient regions a smallspatial error either in the calculation or the measurement results in alarge dose difference between measurement and calculation. Dosedifference in high dose gradient regions may therefore be unimportant,and the concept of distance-to-agreement (DTA) distribution is used todetermine the acceptability of the dose calculation. The distance-toagreement (i.e., geometric) (DTA) criterion (i.e., parameter) is thedistance between a measured data point and the nearest point in thepredicted dose distribution that exhibits the same dose.

To determine dose variations using the gamma evaluation method, therelative dose difference between portal dose images (PDIs) is calculatedby comparing each point in the measured dose distribution with the samepoint in the predicted dose distribution. A dose-difference distributioncan be displayed that identifies the regions where the predicted dosedistributions disagree with the measurement. The gamma evaluation methodis a technique that unifies dose distribution comparisons usingacceptance criteria. The measure of acceptability is themultidimensional distance between the measurement and predicted pointsin both the dose and the physical distance. The gamma value is anumerical quality index that serves as a measure of disagreement in theregions that fail the acceptance criteria and indicates the calculationquality in regions that pass. Gamma values below unity indicateagreement within the passing criteria. The passing criteria for dosedifference criterion and the geometric (Distance to Agreement, DTA)criterion is generally 3% and 3 mm, respectively. The gamma value iscalculated based on these criteria.

Thus, for the conventional two component gamma function, a point istaken in the measured dose, and compared to all points in the predicteddose that fall within a geometrical search box defined by the (DTA). Thepoint in the predicted dose with the lowest gamma index is consideredthe best match.

For example, for two static 3D dose distributions, a dose which ispredicted and is therefore labelled the referenced dose (or searcheddose), and a measured dose which is labelled the compared dose, thegamma index (γ) can be obtained for a point p_(com) in the compared dosevia eq. 3:

$\begin{matrix}{\left( p_{com} \right) = {\min\left\{ \sqrt{\frac{d^{2}\left( {p_{com},{sp}_{ref}} \right)}{{DTA}^{2}} + \frac{\delta^{2}\left( {p_{com},{sp}_{ref}} \right)}{{DD}^{2}}} \right\}{\forall\left\{ {{sp}_{ref}\varepsilon\;\overset{.}{v}} \right\}}}} & (3)\end{matrix}$Where p_(com) is a fixed geometrical point of a voxel in the compareddose; sp_(ref) is any point within the search sphere {circumflex over(υ)} (whose radius=DTA) in the reference dose; and d(p_(com),sp_(ref)),and δ(p_(com),sp_(ref)) are the geometrical distance and dose differencebetween points p_(com) and sp_(pred), respectively. The γ index iscalculated for each voxel in the search sphere {dot over (υ)} and thelowest value kept as the γ value for point p_(com). The process isrepeated for every voxel in the measured dose until a 3D gamma (γ) indexwith the same dimensions as the measured dose is produced.

For quickly changing dynamic data, such as 2D/3D dose distributions ofvarying MLC defined fields, a situation occurs, similar to the high dosegradient regions described above. For each time point, a toxel may finditself inside or outside of the primary field resulting in a high dosegradient in time. This high dose time gradient is also omnipresent inmethods that employ a varying dose-rate scheme, such as VMAT. If astatic comparison is used such as the method described in eq. 3, smalldiscrepancies in timing, arising from imperfect synchronization ordelivery discrepancies will result in large and persistent differencesbetween measurement and calculation. In order to correctly compare in atime-wise fashion, the general gamma function is expanded to take intoconsideration an additional criterion. The additional criterion is thetime to agreement (TTA) criterion, which is selected separately from thegeometric (DTA) criterion. The time-dependent gamma index then becomes:

                                           (4)${\gamma\left( {p_{com},t_{com}} \right)} = {\min\left\{ \sqrt{\frac{d^{2}\left( {p_{com},{sp}_{ref}} \right)}{{DTA}^{2}} + \frac{\delta^{2}\left( {p_{com},{sp}_{ref}} \right)}{{DD}^{2}} + \frac{t^{2}\left( {p_{com},{sp}_{ref}} \right)}{{TTA}^{2}}} \right\}{\forall{\quad\left\{ {{sp}_{ref}\varepsilon\; v} \right\}}}}$

Where t(p_(com), sp_(ref)) is the time difference between pointsp_(com), and sp_(ref). The γ index is calculated for each voxel in the4D search volume υ and the lowest value kept. The process is repeatedfor every toxel (time dependent voxel), meaning it is repeated for everyvoxel in the measured dose at every available time within the data. Thepassing criteria for dose difference criterion, the geometric (DTA)criterion, and the time (TTA) criterion then becomes e.g. 3%, 3 mm, and3 s, respectively. The gamma value is calculated and compared with thesecriteria.

To decrease the time required to calculate a gamma distribution, anordered search box and an early stopping criterion can also be applied.This involves pre-calculating the distances (d) of each vector in thesearch box, disregarding vectors that fall outside the region ofinterest (ROI) sphere, and then the γ index is calculated using thesearch box in ascending order of distance, stopping when (d²/DTA²)exceeds the smallest ((d²/DTA²)+(δ²/DD²)) value found. Using an (ROI)that is much larger than the (DTA), allows gamma values to be calculatedfor two areas: first, the pixels passing the gamma criterion (withinunity); and second, a band of pixels which fail the gamma criterion(exceeding unity). The second area can provide a measure of failure tobe viewed for failing pixels which may inform interpretation.

For a time dependent gamma function, the search box is ordered inascending value of ((d²/DTA²)+(t²/TTA²)) and the γ calculation stopswhen ((d²/DTA²)+(t²/TTA²)) exceeds the smallest((d²/DTA²)+(t²/DD²)+(t²/TTA²)) found. As the number of vectors islargely increased for dynamic data, a more aggressive filtering can beimposed on the search box in order to achieve reasonable calculationtimes. Vectors that exceed 2× unity, based on their position and timealone, i.e. ((d²/DTA²)+(t²/TTA²))>2 can be discarded. This results infar fewer search vectors than discarding vectors based on the region ofinterest (ROI) and time of interest (TOI) alone while still having ameasure of failure outside unity.

To reduce the calculation time, the time-dependent gamma function can beapplied on a graphics processing unit (GPU) as well as a centralprocessing unit (CPU).

Since the time-dependent 4D gamma function uses criteria from eachdimension and searches through the data within the allowed degrees offreedom, it can return an easier to read unity map, as shown in FIG. 10.As shown in FIG. 11, assessment can be made on the amount of toxels(voxels that change over time) exceeding unity to gauge the quality ofthe treatment delivery.

The time-dependent gamma function can provide a practical and reliablecomparison model to robustly compare the measured dose distribution witha predicted dose distribution in a time-dependent manner. Thetime-dependent gamma evaluation method can thus be used for automatederror detection using EPIDs.

The steps of the time-dependent dosimetric verification process 200described herein is as shown in FIG. 12. In Step 1 (S1), 2D portalimages are continuously acquired during a continuous arc beam deliveryusing the electronic portal imaging device (EPID) 20. In Step 2 (S2),the acquired 2D portal images are converted into 2D absolute portal doseimages (PDIs) D_(p)(x, y) using a dosimetric calibration model describedherein. In Step 3 (S3) the image frames are resampled into respectivetime divisions corresponding to the treatment plan's control points(CPs) to obtain a sequence of 2D measured portal dose images, eachmeasured portal dose image in the sequence corresponding to a respective(CP). In Step 4 (S4), each of the measured portal dose images iscompared with a corresponding predicted portal dose image using a 4Dtime-dependent gamma evaluation function, and in Step 5 (S5), the dosedelivery errors are determined based on the comparison by assessing theamount of toxels (voxels that change over time) exceeding unity.

In operation, the series of predicted portal dose images can be storedas a first data set mapped as a first 4D array including positionalinformation and time information of the points in the predicted portaldose images, and the series of measured portal dose images can be storedas a second data set mapped as a second 4D array including positionalinformation and time information of the points in the measured portaldose images. The positional information can include spatial locations ofthe predicted and measured radiation fields, and the time informationcan include the delivery times of the predicted and measured radiationfields. The comparing of measured and predicted dose distributions thenincludes comparing the first and second data sets using a 4Dtime-dependent gamma evaluation method by which the positionaldifferences between corresponding points in the first and second datasets and the time differences between corresponding points in the firstand second data sets are measured.

A non-transitory computer readable medium can be used to store thesoftware or programmed instructions and data which when executed by acomputer processing system 40 causes the system to perform variousmethods of the present invention, as discussed herein. The executablesoftware and data may be stored in various places, including, forexample, the memory and storage of the computer processing system 40 orany other device that is capable of storing software and/or data.

Accordingly, embodiments of quality control systems, methods andcomputer program products for time-dependent pre-treatment dosimetricevaluations have been disclosed. Many alternatives, modifications, andvariations are enabled by the present disclosure. Features of thedisclosed embodiments can be combined, rearranged, omitted, etc. withinthe scope of the invention to produce additional embodiments.

Embodiments also provide time-dependent pre-treatment dosimetricevaluation methods for an arc-based radiation therapy device,comprising: generating a sequence of portal dose images based on imagescontinuously acquired during beam delivery according to a treatmentplan, each portal dose image in the sequence corresponding to a measureddose delivered at a predetermined time interval of the treatment plan;for each predetermined time interval, comparing the generated portaldose image with a corresponding predicted portal dose image; andevaluating dose delivery based on the comparison.

Embodiments also provide methods for verifying quantities of interest ofa radiation beam in an arc-based radiation therapy device, comprising:continuously acquiring portal images during a continuous arc beamdelivery using the electronic portal imaging device (EPID); convertingthe acquired portal images into absolute portal dose images using adosimetric calibration model; resampling the absolute portal dose imagesinto respective time divisions corresponding to a treatment plan'scontrol points (CPs) to obtain a sequence of measured portal doseimages, each measured portal dose image in the sequence corresponding toa respective (CP); comparing each of the measured portal dose images inthe sequence with corresponding predicted portal dose images using a 4Dtime-dependent gamma evaluation function; and determine dose deliveryerrors based on the comparison by assessing the amount of voxels thatchange over time exceeding unity.

Embodiments also provide systems for verifying quantities of interest ofa radiation beam in an arc-based radiation therapy device including agantry, comprising: a portal imaging device configured to measureincident radiation dose from predetermined radiation fieldscorresponding to an arc segment of the gantry and to generate a seriesof two-dimensional (2D) absolute portal dose images using a calibrationmodel; and a processing device operably connected to the portal doseimaging device and configured to store the series of 2D absolute portaldose images in a first 4D array having spatial locations and time ofdelivery of the predetermined radiation fields as dimensions, theprocessing device being further configured to store a series ofpredicted 2D portal dose images in a second 4D array having spatiallocations and time of delivery of predicted radiation fields asdimensions; wherein the processing device is further configured tocompare points in the first 4D array with corresponding points in thesecond 4D array using a 4D gamma evaluation method, the gamma evaluationmethod including dose differences, spatial differences, and timedifferences between corresponding points in the first and second 4Darrays as parameters, wherein errors in the quantities of interest aredetermined based on the comparison.

Embodiments also provide a non-transitory computer-readable storagemedium upon which is embodied a sequence of programmed instructions forquality control in a radiation therapy treatment system as disclosedherein, including a computer processing system, as disclosed herein,which executes the sequence of programmed instructions embodied on thecomputer-readable storage medium to cause the computer processing systemto perform the steps of the methods as disclosed herein.

Furthermore, certain features of the disclosed embodiments may sometimebe used to advantage without a corresponding use of other features.Accordingly, Applicants intend to embrace all such alternatives,modifications, equivalents, and variations that are within the spiritand scope of the present disclosure.

While embodiments and applications of this invention have been shown anddescribed, it would be apparent to those skilled in the art that manymore modifications are possible without departing from the inventiveconcepts herein. The invention is not limited to the description of theembodiments contained herein, but rather is defined by the claimsappended hereto and their equivalents.

What is claimed is:
 1. A dosimetric evaluation method, comprising:generating a sequence of portal dose images, the portal dose images inthe sequence corresponding to measured radiation doses deliveredaccording to a radiation treatment plan; comparing the portal doseimages generated with corresponding predicted portal dose images using atime-dependent gamma evaluation method; and evaluating radiation dosedelivery based on the comparison.
 2. The method of claim 1, wherein themeasured radiation doses are delivered at predetermined time intervalsof the radiation treatment plan, and the comparing includes comparingthe portal dose images generated for respective time intervals withcorresponding predicted portal dose images.
 3. The method of claim 2,wherein the time intervals correspond to respective control points (CPs)of the radiation treatment plan.
 4. The method of claim 1, wherein thecomparing comprises using the time-dependent gamma evaluation method tocalculate dose differences, spatial differences, and time differencesbetween points in the measured portal dose images and correspondingpoints in the predicted portal dose images.
 5. The method of claim 1,wherein the evaluating includes determining dose delivery errors.
 6. Amethod for verifying quantities of interest of a delivered radiationbeam, comprising: acquiring portal dose images during radiation beamdelivery; resampling the acquired portal dose images into respectivetime divisions corresponding to a radiation treatment plan's controlpoints (CPs) to obtain a sequence of measured portal dose images, themeasured portal dose images in the sequence corresponding to respectivecontrol points (CPs); comparing the measured portal dose images in thesequence with corresponding predicted portal dose images using atime-dependent gamma evaluation method; and determining dose deliveryerrors based on the comparison.
 7. The method of claim 6, wherein thecomparing comprises using the time-dependent gamma evaluation method tocalculate dose differences, spatial differences, and time differencesbetween points in the measured portal dose images and correspondingpoints in the predicted portal dose images.
 8. The method of claim 7,wherein the comparing includes computing a time-dependent gamma errormatrix based on a gamma error function including time as a fourthdimension.
 9. The method of claim 6, wherein the evaluating includesdetermining dose delivery errors.
 10. A system for verifying a quantityof interest of a radiation beam, comprising: a portal imaging deviceconfigured to measure incident radiation dose from predeterminedradiation fields and to generate a series of portal dose images; and aprocessing device operably connected to the portal dose imaging deviceand configured to store the series of portal dose images in a first dataarray having spatial locations and time of delivery of the predeterminedradiation fields as dimensions, the processing device being furtherconfigured to store a series of predicted portal dose images in a seconddata array having spatial locations and time of delivery of predictedradiation fields as dimensions, wherein the processing device is furtherconfigured to compare points in the first data array with correspondingpoints in the second data array using a 4D gamma evaluation method, thegamma evaluation method including dose differences, spatial differences,and time differences between corresponding points in the first andsecond data arrays as parameters, and wherein errors in the quantitiesof interest are determined based on the comparison.
 11. The system ofclaim 10, wherein the quantity of interest is of a radiation beam usedin an arc-field radiation therapy device.
 12. The system of claim 11,wherein the arc-field radiation therapy device is one of an intensitymodulated radiation therapy device and a volumetric modulated arctherapy device.
 13. The system of claim 10, wherein the portal imagingdevice is an electronic portal imaging device (EPID).
 14. The system ofclaim 10, wherein the quantity of interest is radiation dose.
 15. Asystem including a radiation therapy device and a computer processingsystem which executes a sequence of programmed instructions embodied ona computer-readable storage medium to cause the computer processingsystem to: initiate radiation beam delivery in the radiation therapydevice according to a radiation treatment delivery plan; acquire portaldose images during the radiation beam delivery; resample the portal doseimages into respective time divisions corresponding to the radiationtreatment plan's control points (CPs) to obtain a sequence of measuredportal dose images, each measured portal dose image in the sequencecorresponding to a respective control point (CP); compare the measuredportal dose images in the sequence with corresponding predicted portaldose images using a time-dependent gamma evaluation method; anddetermine radiation dose delivery errors based on the comparison. 16.The system of claim 15, wherein the time-dependent gamma evaluationmethod includes a 4D evaluation method including time as the fourthdimension.
 17. The system of claim 15, wherein the comparing comprisesusing the time-dependent gamma evaluation method to calculate dosedifferences, spatial differences, and time differences between points inthe measured portal dose images and corresponding points in thepredicted portal dose images as parameters.
 18. The system of claim 17,further including storing the predicted portal dose images as a firstdata set including positional information and time information of thepoints in the predicted portal dose images as parameters, and storingthe measured portal dose images as a second data set includingpositional information and time information of the points in themeasured portal dose images as parameters.
 19. The system of claim 18,wherein the positional information includes spatial locations of thepredicted and measured radiation fields, and the time informationincludes the times of the predicted and measured radiation fields, andwherein the comparing includes comparing the first and second data setsusing the time-dependent gamma evaluation method.
 20. The system ofclaim 19, wherein the comparing includes computing a time-dependentgamma error matrix based on a gamma error function:${\gamma\left( {p_{com},t_{com}} \right)} = {\min\left\{ \sqrt{\frac{d^{2}\left( {p_{com},{sp}_{ref}} \right)}{{DTA}^{2}} + \frac{\delta^{2}\left( {p_{com},{sp}_{ref}} \right)}{{DD}^{2}} + \frac{t^{2}\left( {p_{com},{sp}_{ref}} \right)}{{TTA}^{2}}} \right\}{\forall\left\{ {{sp}_{ref}\varepsilon\; v} \right\}}}$wherein the gamma value γ in the gamma error matrix is a minimum valueof a positional metric and a time metric indicating a measure ofacceptability of a quantity of interest.